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\([\) Sec. REF \(]\)  LOCA\(_{VAE}\)  and LOCA\(_{EASE}\)  achieve state-of-the-art results with an improvement of up to 4.70% (Recall) and 7.95% (NDCG) over the existing models, irrespective of whether they are global or local models.

\([\) Sec. REF \(]\)  As the number of local models increases, LOCA\(_{VAE}\)  shows better performance. With up to 300 local models, it achieves up to 8.63% improvement gain in NDCG@100 as compared to MultVAE [1]}.

\([\) Sec. REF \(]\)  The coverage-based anchor selection in LOCA\(_{VAE}\)  outperforms the other anchor selection methods (an improvement of up to 1.70% in NDCG@100 over the random selection).

\([\) Sec. REF \(]\)  LOCA can be integrated with other AE-based models, and the variant of LOCA also consistently outperforms the base models (an improvement of up to 5.39% in NDCG@100 relative to the global model).

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